Carregant...
Fitxers
Tipus de document
TesiVersió
Versió publicadaData de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/207990
Testing for exact model fit and model comparison in structural equation modeling under non-normality
Títol de la revista
Autors
ISSN de la revista
Títol del volum
Resum
[eng] Structural equation modeling (SEM) is a versatile framework that allows researchers to
estimate systems of equations and test theoretical models. A significant portion of the literature
on SEM focuses on model fit and selection, where researchers are interested in evaluating the
goodness of fit of a theoretical model (absolute fit) or comparing multiple plausible models
(relative fit). Evaluating exact or approximate fit is possible in both cases. The current doctoral
thesis is a compilation of two published studies that contribute to the literature on both absolute
and relative fit.
The first study aimed to compare the accuracy of assessing exact model fit using two tests,
namely the mean and variance adjusted chi-square test and the recently developed robust
version of the Standardized Root Mean Squared Residual (SRMR) test, in situations where data
is not normal. Through simulation, the study examined the impact of factors such as
(non)normality, sample size, and model size on test accuracy. The results showed that the
robust chi-square test outperformed the robust SRMR test with respect to Type I error rates and
was less affected by model size.
The second study investigated the accuracy of evaluating relative model fit using several
versions of chi-square difference tests that are robust to violations of normality. The study
manipulated levels of (non)normality, sample size, model size, and degrees of freedom for the
difference test through simulation. The results showed that the mean and variance adjusted chisquare
difference test performed accurately across all investigated conditions and outperformed
its mean-adjusted competitors, which required larger samples to perform adequately.
In summary, the two studies in the doctoral thesis contribute to the literature on both absolute
and relative fit in SEM. The findings suggest that the robust chi-square test is more accurate in
assessing exact model fit than the robust SRMR test, and the mean and variance adjusted chi-square
difference test is a reliable method for evaluating relative model fit in SEM.
Descripció
Matèries
Matèries (anglès)
Citació
Citació
PAVLOV, Goran. Testing for exact model fit and model comparison in structural equation modeling under non-normality. [consulta: 29 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/207990]